Open in Clarusway LMS

Open Data Analysis with Python in Clarusway LMS

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General Regression Model Training

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Importing Related Libraries

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Model Training by For Loop:

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.csv dosyalarının isimleri aşağıda olduğu gibi düzenlenmeli.¢

final_scout_dummy_EmreDA8127

Γ–ΔŸrenci isimleri : ['TugceDA8122', 'TugceDA8122', 'AsliDA8115', 'AysegulDA8116', 'DamlaDA8120', 'EmreDA8119', 'EmreDA8127', 'EsraDA8133', 'GyulferaDA8131', 'HasanDA8121', 'NurdanDA8123', 'SerahsiDA8135', 'SezerDA8134']



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  TugceDA8122's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage power gears age make_model_encoded make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other seller_Private_seller emission_class_Other previous_owner_Second_Hand entertainment_media_count_Upgrated engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium comfort_convenience_cat_standard
0 16950 120200.00000 75.00000 6.00000 6.00000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1
1 46990 18995.00000 225.00000 7.00000 2.00000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
2 16800 197000.00000 100.00000 7.00000 7.00000 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 1 1
3 4690 165000.00000 90.00000 6.00000 17.00000 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1 1
4 22550 83339.00000 90.00000 7.00000 4.00000 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21602 entries, 0 to 21601
Data columns (total 36 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   price                               21602 non-null  int64  
 1   mileage                             21602 non-null  float64
 2   power                               21602 non-null  float64
 3   gears                               21602 non-null  float64
 4   age                                 21602 non-null  float64
 5   make_model_encoded                  21602 non-null  int64  
 6   make_Fiat                           21602 non-null  int64  
 7   make_Ford                           21602 non-null  int64  
 8   make_Hyundai                        21602 non-null  int64  
 9   make_Mercedes_Benz                  21602 non-null  int64  
 10  make_Nissan                         21602 non-null  int64  
 11  make_Opel                           21602 non-null  int64  
 12  make_Peugeot                        21602 non-null  int64  
 13  make_Renault                        21602 non-null  int64  
 14  make_Seat                           21602 non-null  int64  
 15  make_Skoda                          21602 non-null  int64  
 16  make_Toyota                         21602 non-null  int64  
 17  make_Volvo                          21602 non-null  int64  
 18  body_type_Convertible               21602 non-null  int64  
 19  body_type_Coupe                     21602 non-null  int64  
 20  body_type_Off-Road/Pick-up          21602 non-null  int64  
 21  body_type_Sedan                     21602 non-null  int64  
 22  body_type_Station_wagon             21602 non-null  int64  
 23  gearbox_Manual                      21602 non-null  int64  
 24  fuel_type_Diesel                    21602 non-null  int64  
 25  fuel_type_Electric                  21602 non-null  int64  
 26  fuel_type_LPG/CNG                   21602 non-null  int64  
 27  fuel_type_Other                     21602 non-null  int64  
 28  seller_Private_seller               21602 non-null  int64  
 29  emission_class_Other                21602 non-null  int64  
 30  previous_owner_Second_Hand          21602 non-null  int64  
 31  entertainment_media_count_Upgrated  21602 non-null  int64  
 32  engine_size_cat_High                21602 non-null  int64  
 33  engine_size_cat_Low                 21602 non-null  int64  
 34  engine_size_cat_Medium              21602 non-null  int64  
 35  comfort_convenience_cat_standard    21602 non-null  int64  
dtypes: float64(4), int64(32)
memory usage: 5.9 MB

None
(21602, 36)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.85508        0.85632
mae      3452.91793     3399.24570
mse  23715664.27028 23115516.77038
rmse     4869.87313     4807.85990



HuberRegression Model Metrics :

          train_set       test_set
R2          0.84861        0.84937
mae      3357.39012     3311.95373
mse  24774956.35866 24233279.80672
rmse     4977.44476     4922.73093



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.85508        0.85632
mae      3452.91793     3399.24570
mse  23715664.27028 23115516.77038
rmse     4869.87313     4807.85990



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99984        0.87690
mae     15.27296     2900.66893
mse  26949.37939 19805002.80999
rmse   164.16266     4450.28121



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92849        0.91197
mae      2313.65316     2530.44463
mse  11703165.80525 14162022.21872
rmse     3420.98901     3763.24623



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.63879        0.63449
mae      6775.20915     6736.17485
mse  59112743.04705 58804097.64032
rmse     7688.48119     7668.38299



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.91456        0.91171
mae      2563.09153     2598.49437
mse  13981555.99678 14203716.89208
rmse     3739.19189     3768.78188



XG Boosting Regressor Model Metrics :

         train_set      test_set
R2         0.97416       0.94129
mae     1507.28322    2067.75065
mse  4229389.76601 9445054.93553
rmse    2056.54802    3073.28081



[LightGBM] [Info] Total Bins 761
[LightGBM] [Info] Number of data points in the train set: 15121, number of used features: 35
[LightGBM] [Info] Start training from score 20967.628001



Light GBM Regressor Model Metrics :

         train_set       test_set
R2         0.94907        0.93457
mae     2010.01456     2187.71608
mse  8335045.15868 10525974.38380
rmse    2887.04783     3244.37581



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.85505        0.85621
mae      3453.86343     3400.94291
mse  23721368.33803 23133037.92725
rmse     4870.45874     4809.68169



Cat Boost Linear Ridge Model Metrics :

         train_set      test_set
R2         0.96224       0.94585
mae     1772.59354    2017.19474
mse  6178933.23096 8712530.83483
rmse    2485.74601    2951.69965



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 6775.20915 3453.86343 1772.59354 15.27296 2563.09153 3357.39012 2010.01456 3452.91793 2313.65316 3452.91793 1507.28322
MSE Score 59112743.04705 23721368.33803 6178933.23096 26949.37939 13981555.99678 24774956.35866 8335045.15868 23715664.27028 11703165.80525 23715664.27028 4229389.76601
R2 Score 0.63449 0.85621 0.94585 0.87690 0.91171 0.84937 0.93457 0.85632 0.91197 0.85632 0.94129
RMSE Score 7668.38299 4809.68169 2951.69965 4450.28121 3768.78188 4922.73093 3244.37581 4807.85990 3763.24623 4807.85990 3073.28081
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  TugceDA8122's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price doors mileage seats power gears co_emissions age fuel_consumption_comb make_model_encoded make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo location_BE location_BG location_DE location_ES location_FR location_IT location_LU location_NL body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon type_Employee's_car type_Pre-registered type_Used warranty_Yes gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other paint_non-metallic seller_Private_seller drivetrain_Front drivetrain_Rear full_service_history_Yes non_smoker_vehicle_Yes emission_class_Euro_2 emission_class_Euro_3 emission_class_Euro_4 emission_class_Euro_5 emission_class_Euro_6 upholstery_Leather upholstery_Other previous_owner_Second_Hand entertainment_media_count_Upgrated engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium safety_security_category_Middle safety_security_category_Premium extras_category_standard comfort_convenience_cat_standard
0 16950 5.00000 120200.00000 5.00000 75.00000 6.00000 101.00000 6.00000 5.40000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1
1 46990 5.00000 18995.00000 5.00000 225.00000 7.00000 170.00000 2.00000 7.30000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 1 1
2 16800 5.00000 197000.00000 5.00000 100.00000 7.00000 82.00000 7.00000 4.90000 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1 1
3 4690 3.00000 165000.00000 4.00000 90.00000 6.00000 196.00000 17.00000 8.20000 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 1
4 22550 5.00000 83339.00000 5.00000 90.00000 7.00000 94.00000 4.00000 6.30000 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20801 entries, 0 to 20800
Data columns (total 66 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   price                               20801 non-null  int64  
 1   doors                               20801 non-null  float64
 2   mileage                             20801 non-null  float64
 3   seats                               20801 non-null  float64
 4   power                               20801 non-null  float64
 5   gears                               20801 non-null  float64
 6   co_emissions                        20801 non-null  float64
 7   age                                 20801 non-null  float64
 8   fuel_consumption_comb               20801 non-null  float64
 9   make_model_encoded                  20801 non-null  int64  
 10  make_Fiat                           20801 non-null  int64  
 11  make_Ford                           20801 non-null  int64  
 12  make_Hyundai                        20801 non-null  int64  
 13  make_Mercedes_Benz                  20801 non-null  int64  
 14  make_Nissan                         20801 non-null  int64  
 15  make_Opel                           20801 non-null  int64  
 16  make_Peugeot                        20801 non-null  int64  
 17  make_Renault                        20801 non-null  int64  
 18  make_Seat                           20801 non-null  int64  
 19  make_Skoda                          20801 non-null  int64  
 20  make_Toyota                         20801 non-null  int64  
 21  make_Volvo                          20801 non-null  int64  
 22  location_BE                         20801 non-null  int64  
 23  location_BG                         20801 non-null  int64  
 24  location_DE                         20801 non-null  int64  
 25  location_ES                         20801 non-null  int64  
 26  location_FR                         20801 non-null  int64  
 27  location_IT                         20801 non-null  int64  
 28  location_LU                         20801 non-null  int64  
 29  location_NL                         20801 non-null  int64  
 30  body_type_Convertible               20801 non-null  int64  
 31  body_type_Coupe                     20801 non-null  int64  
 32  body_type_Off-Road/Pick-up          20801 non-null  int64  
 33  body_type_Sedan                     20801 non-null  int64  
 34  body_type_Station_wagon             20801 non-null  int64  
 35  type_Employee's_car                 20801 non-null  int64  
 36  type_Pre-registered                 20801 non-null  int64  
 37  type_Used                           20801 non-null  int64  
 38  warranty_Yes                        20801 non-null  int64  
 39  gearbox_Manual                      20801 non-null  int64  
 40  fuel_type_Diesel                    20801 non-null  int64  
 41  fuel_type_Electric                  20801 non-null  int64  
 42  fuel_type_LPG/CNG                   20801 non-null  int64  
 43  fuel_type_Other                     20801 non-null  int64  
 44  paint_non-metallic                  20801 non-null  int64  
 45  seller_Private_seller               20801 non-null  int64  
 46  drivetrain_Front                    20801 non-null  int64  
 47  drivetrain_Rear                     20801 non-null  int64  
 48  full_service_history_Yes            20801 non-null  int64  
 49  non_smoker_vehicle_Yes              20801 non-null  int64  
 50  emission_class_Euro_2               20801 non-null  int64  
 51  emission_class_Euro_3               20801 non-null  int64  
 52  emission_class_Euro_4               20801 non-null  int64  
 53  emission_class_Euro_5               20801 non-null  int64  
 54  emission_class_Euro_6               20801 non-null  int64  
 55  upholstery_Leather                  20801 non-null  int64  
 56  upholstery_Other                    20801 non-null  int64  
 57  previous_owner_Second_Hand          20801 non-null  int64  
 58  entertainment_media_count_Upgrated  20801 non-null  int64  
 59  engine_size_cat_High                20801 non-null  int64  
 60  engine_size_cat_Low                 20801 non-null  int64  
 61  engine_size_cat_Medium              20801 non-null  int64  
 62  safety_security_category_Middle     20801 non-null  int64  
 63  safety_security_category_Premium    20801 non-null  int64  
 64  extras_category_standard            20801 non-null  int64  
 65  comfort_convenience_cat_standard    20801 non-null  int64  
dtypes: float64(8), int64(58)
memory usage: 10.5 MB

None
(20801, 66)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.87753        0.87220
mae      3082.35417     3129.28875
mse  18807814.86400 19668927.49323
rmse     4336.79777     4434.96646



HuberRegression Model Metrics :

          train_set       test_set
R2          0.87171        0.86683
mae      3022.17174     3054.74900
mse  19700285.36178 20495232.13903
rmse     4438.50035     4527.16602



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.87753        0.87220
mae      3082.35417     3129.28875
mse  18807814.86400 19668927.49323
rmse     4336.79777     4434.96646



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99992        0.86982
mae      7.58624     2869.40210
mse  12890.77297 20035011.54336
rmse   113.53754     4476.04865



Random Forest Regressor Model Metrics :

         train_set       test_set
R2         0.93502        0.90871
mae     2175.49417     2508.59654
mse  9978510.73750 14049578.99558
rmse    3158.87808     3748.27680



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.72520        0.71634
mae      5626.33088     5699.63110
mse  42199273.43863 43656778.34954
rmse     6496.09678     6607.32763



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.92435        0.91768
mae      2381.63989     2440.16997
mse  11617920.78296 12669818.91848
rmse     3408.50712     3559.46891



XG Boosting Regressor Model Metrics :

         train_set      test_set
R2         0.98019       0.94349
mae     1288.70307    1957.09445
mse  3042386.04796 8697665.80240
rmse    1744.24369    2949.18053



[LightGBM] [Info] Total Bins 1058
[LightGBM] [Info] Number of data points in the train set: 14560, number of used features: 61
[LightGBM] [Info] Start training from score 20553.206868



Light GBM Regressor Model Metrics :

         train_set      test_set
R2         0.95952       0.94123
mae     1765.94161    2034.62048
mse  6216262.28004 9045618.50175
rmse    2493.24333    3007.59347



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.87741        0.87203
mae      3085.13484     3130.23658
mse  18825092.92124 19695935.93407
rmse     4338.78934     4438.01036



Cat Boost Linear Ridge Model Metrics :

         train_set      test_set
R2         0.97186       0.95003
mae     1505.74803    1845.39427
mse  4320956.56068 7691212.31580
rmse    2078.69107    2773.30350



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 5626.33088 3085.13484 1505.74803 7.58624 2381.63989 3022.17174 1765.94161 3082.35417 2175.49417 3082.35417 1288.70307
MSE Score 42199273.43863 18825092.92124 4320956.56068 12890.77297 11617920.78296 19700285.36178 6216262.28004 18807814.86400 9978510.73750 18807814.86400 3042386.04796
R2 Score 0.71634 0.87203 0.95003 0.86982 0.91768 0.86683 0.94123 0.87220 0.90871 0.87220 0.94349
RMSE Score 6607.32763 4438.01036 2773.30350 4476.04865 3559.46891 4527.16602 3007.59347 4434.96646 3748.27680 4434.96646 2949.18053
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  AsliDA8115's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions power_kW fuel_consumption_comb age make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 96 5 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 110 6 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 110 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 51 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   power_kW                              24529 non-null  int64
 7   fuel_consumption_comb                 24529 non-null  int64
 8   age                                   24529 non-null  int64
 9   make_model_factorized                 24529 non-null  int64
 10  body_type__Convertible                24529 non-null  int64
 11  body_type__Coupe                      24529 non-null  int64
 12  body_type__Off-Road/Pick-up           24529 non-null  int64
 13  body_type__Sedan                      24529 non-null  int64
 14  body_type__Station_wagon              24529 non-null  int64
 15  warranty_Yes                          24529 non-null  int64
 16  gearbox_Manual                        24529 non-null  int64
 17  gearbox_Semi-automatic                24529 non-null  int64
 18  fuel_type_Diesel                      24529 non-null  int64
 19  fuel_type_Electric                    24529 non-null  int64
 20  fuel_type_LPG                         24529 non-null  int64
 21  colour_Black                          24529 non-null  int64
 22  colour_Blue                           24529 non-null  int64
 23  colour_Bronze                         24529 non-null  int64
 24  colour_Brown                          24529 non-null  int64
 25  colour_Gold                           24529 non-null  int64
 26  colour_Green                          24529 non-null  int64
 27  colour_Grey                           24529 non-null  int64
 28  colour_Orange                         24529 non-null  int64
 29  colour_Red                            24529 non-null  int64
 30  colour_Silver                         24529 non-null  int64
 31  colour_Violet                         24529 non-null  int64
 32  colour_White                          24529 non-null  int64
 33  colour_Yellow                         24529 non-null  int64
 34  drivetrain_Front                      24529 non-null  int64
 35  drivetrain_Rear                       24529 non-null  int64
 36  non_smoker_Yes                        24529 non-null  int64
 37  emission_sticker_No_sticker           24529 non-null  int64
 38  emission_sticker_Red                  24529 non-null  int64
 39  emission_sticker_Yellow               24529 non-null  int64
 40  upholstery_Full_leather               24529 non-null  int64
 41  upholstery_Other                      24529 non-null  int64
 42  upholstery_Part_leather               24529 non-null  int64
 43  upholstery_Velour                     24529 non-null  int64
 44  upholstery_alcantara                  24529 non-null  int64
 45  safety_security_package_Basic         24529 non-null  int64
 46  safety_security_package_Enhanced      24529 non-null  int64
 47  comfort_convenience_package_Basic     24529 non-null  int64
 48  comfort_convenience_package_Enhanced  24529 non-null  int64
 49  ent_media_package_Basic               24529 non-null  int64
 50  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(51)
memory usage: 9.5 MB

None
(24529, 51)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



HuberRegression Model Metrics :

          train_set       test_set
R2          0.74842        0.75294
mae      4644.22247     4622.21707
mse  56492043.07455 55071902.37974
rmse     7516.11888     7421.04456



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.84257
mae     1.14543     3307.79947
mse  1328.78718 35092847.19058
rmse   36.45253     5923.92161



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92145        0.88511
mae      2565.42971     2993.96732
mse  17639509.41618 25610097.71287
rmse     4199.94160     5060.64203



Ada Boost Regressor Model Metrics :

          train_set        test_set
R2          0.55878         0.53899
mae      8411.11711      8462.59512
mse  99077157.86718 102761542.31176
rmse     9953.75094     10137.13679



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.89748        0.87252
mae      3061.44094     3244.39116
mse  23020963.52384 28416434.69317
rmse     4798.01662     5330.70677



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.97580        0.91265
mae     1657.36035     2539.00851
mse  5433075.53408 19469922.88249
rmse    2330.89587     4412.47356



[LightGBM] [Info] Total Bins 1322
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

          train_set       test_set
R2          0.94720        0.90983
mae      2283.47161     2674.31067
mse  11855808.80354 20100010.33039
rmse     3443.22651     4483.30351



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.76655        0.76659
mae      4839.14450     4850.98532
mse  52421491.42523 52028553.03376
rmse     7240.26874     7213.08208



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.96380        0.92139
mae     1976.12172     2477.60347
mse  8127895.32365 17521714.02079
rmse    2850.94639     4185.89465



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 8411.11711 4839.14450 1976.12172 1.14543 3061.44094 4644.22247 2283.47161 4841.22970 2565.42971 4841.22970 1657.36035
MSE Score 99077157.86718 52421491.42523 8127895.32365 1328.78718 23020963.52384 56492043.07455 11855808.80354 52412344.82297 17639509.41618 52412344.82297 5433075.53408
R2 Score 0.53899 0.76659 0.92139 0.84257 0.87252 0.75294 0.90983 0.76651 0.88511 0.76651 0.91265
RMSE Score 10137.13679 7213.08208 4185.89465 5923.92161 5330.70677 7421.04456 4483.30351 7214.29080 5060.64203 7214.29080 4412.47356
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  AysegulDA8116's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

!!!!!!!!AysegulDA8116's DataFrame has non-numeric value(s) !!!!!!!!!!






<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  DamlaDA8120's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions cylinders power_kW fuel_consumption_comb age z_score_gears price_zscore mileage_zscore cylinders_zscore z_score make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara location_country_Belgium location_country_Bulgaria location_country_Denmark location_country_Estonia location_country_France location_country_Germany location_country_Italy location_country_Luxembourg location_country_Netherlands location_country_Spain safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 4 85 4 0 0 0 -1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 4 85 4 0 0 0 -1 0 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 4 96 5 0 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 4 110 6 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 4 110 5 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 67 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   cylinders                             24529 non-null  int64
 7   power_kW                              24529 non-null  int64
 8   fuel_consumption_comb                 24529 non-null  int64
 9   age                                   24529 non-null  int64
 10  z_score_gears                         24529 non-null  int64
 11  price_zscore                          24529 non-null  int64
 12  mileage_zscore                        24529 non-null  int64
 13  cylinders_zscore                      24529 non-null  int64
 14  z_score                               24529 non-null  int64
 15  make_model_factorized                 24529 non-null  int64
 16  body_type__Convertible                24529 non-null  int64
 17  body_type__Coupe                      24529 non-null  int64
 18  body_type__Off-Road/Pick-up           24529 non-null  int64
 19  body_type__Sedan                      24529 non-null  int64
 20  body_type__Station_wagon              24529 non-null  int64
 21  warranty_Yes                          24529 non-null  int64
 22  gearbox_Manual                        24529 non-null  int64
 23  gearbox_Semi-automatic                24529 non-null  int64
 24  fuel_type_Diesel                      24529 non-null  int64
 25  fuel_type_Electric                    24529 non-null  int64
 26  fuel_type_LPG                         24529 non-null  int64
 27  colour_Black                          24529 non-null  int64
 28  colour_Blue                           24529 non-null  int64
 29  colour_Bronze                         24529 non-null  int64
 30  colour_Brown                          24529 non-null  int64
 31  colour_Gold                           24529 non-null  int64
 32  colour_Green                          24529 non-null  int64
 33  colour_Grey                           24529 non-null  int64
 34  colour_Orange                         24529 non-null  int64
 35  colour_Red                            24529 non-null  int64
 36  colour_Silver                         24529 non-null  int64
 37  colour_Violet                         24529 non-null  int64
 38  colour_White                          24529 non-null  int64
 39  colour_Yellow                         24529 non-null  int64
 40  drivetrain_Front                      24529 non-null  int64
 41  drivetrain_Rear                       24529 non-null  int64
 42  non_smoker_Yes                        24529 non-null  int64
 43  emission_sticker_No_sticker           24529 non-null  int64
 44  emission_sticker_Red                  24529 non-null  int64
 45  emission_sticker_Yellow               24529 non-null  int64
 46  upholstery_Full_leather               24529 non-null  int64
 47  upholstery_Other                      24529 non-null  int64
 48  upholstery_Part_leather               24529 non-null  int64
 49  upholstery_Velour                     24529 non-null  int64
 50  upholstery_alcantara                  24529 non-null  int64
 51  location_country_Belgium              24529 non-null  int64
 52  location_country_Bulgaria             24529 non-null  int64
 53  location_country_Denmark              24529 non-null  int64
 54  location_country_Estonia              24529 non-null  int64
 55  location_country_France               24529 non-null  int64
 56  location_country_Germany              24529 non-null  int64
 57  location_country_Italy                24529 non-null  int64
 58  location_country_Luxembourg           24529 non-null  int64
 59  location_country_Netherlands          24529 non-null  int64
 60  location_country_Spain                24529 non-null  int64
 61  safety_security_package_Basic         24529 non-null  int64
 62  safety_security_package_Enhanced      24529 non-null  int64
 63  comfort_convenience_package_Basic     24529 non-null  int64
 64  comfort_convenience_package_Enhanced  24529 non-null  int64
 65  ent_media_package_Basic               24529 non-null  int64
 66  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(67)
memory usage: 12.5 MB

None
(24529, 67)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.87819        0.87777
mae      3867.34385     3860.60787
mse  27352551.31604 27246445.88006
rmse     5229.96666     5219.81282



HuberRegression Model Metrics :

          train_set       test_set
R2          0.87139        0.87473
mae      3779.31754     3728.59401
mse  28880527.45064 27923686.25613
rmse     5374.06061     5284.28673



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.87819        0.87777
mae      3867.34385     3860.60787
mse  27352551.31604 27246445.88006
rmse     5229.96666     5219.81282



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.90445
mae     1.14543     2899.39829
mse  1328.78718 21298712.39190
rmse   36.45253     4615.05280



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.95232        0.93459
mae      2293.86789     2629.52368
mse  10707556.83618 14579689.96813
rmse     3272.24034     3818.33602



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.82975        0.82493
mae      5180.06950     5192.50295
mse  38230725.55051 39023099.56670
rmse     6183.10000     6246.84717



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.93837        0.93013
mae      2639.61364     2762.50707
mse  13838441.07150 15573666.65758
rmse     3720.00552     3946.34852



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.98262        0.95179
mae     1445.73207     2138.41078
mse  3903532.46051 10746033.93823
rmse    1975.73593     3278.11439



[LightGBM] [Info] Total Bins 1372
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 63
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

         train_set       test_set
R2         0.96448        0.94833
mae     2005.60132     2291.96333
mse  7976521.64817 11516896.84416
rmse    2824.27365     3393.65538



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.87812        0.87782
mae      3868.10088     3859.50166
mse  27367611.17300 27234698.09598
rmse     5231.40623     5218.68739



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.97412        0.95265
mae     1743.70527     2150.43883
mse  5812190.67280 10554721.84414
rmse    2410.84854     3248.80314



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 5180.06950 3868.10088 1743.70527 1.14543 2639.61364 3779.31754 2005.60132 3867.34385 2293.86789 3867.34385 1445.73207
MSE Score 38230725.55051 27367611.17300 5812190.67280 1328.78718 13838441.07150 28880527.45064 7976521.64817 27352551.31604 10707556.83618 27352551.31604 3903532.46051
R2 Score 0.82493 0.87782 0.95265 0.90445 0.93013 0.87473 0.94833 0.87777 0.93459 0.87777 0.95179
RMSE Score 6246.84717 5218.68739 3248.80314 4615.05280 3946.34852 5284.28673 3393.65538 5219.81282 3818.33602 5219.81282 3278.11439
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EmreDA8119's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price doors mileage power gears age make_model_encoded make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo location_BE location_BG location_DE location_ES location_FR location_IT location_LU location_NL body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon type_Employee's_car type_Pre-registered type_Used gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other seller_Private_seller emission_class_Other entertainment_media_count_Upgrated engine_size_cat_Extreme engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium comfort_convenience_cat_standard
0 16950 5.00000 120200.00000 75.00000 6.00000 6.00000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1
1 46990 5.00000 18995.00000 225.00000 7.00000 2.00000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1
2 16800 5.00000 197000.00000 100.00000 7.00000 7.00000 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 1
3 4690 3.00000 165000.00000 90.00000 6.00000 17.00000 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1
4 22550 5.00000 83339.00000 90.00000 7.00000 4.00000 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21617 entries, 0 to 21616
Data columns (total 48 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   price                               21617 non-null  int64  
 1   doors                               21617 non-null  float64
 2   mileage                             21617 non-null  float64
 3   power                               21617 non-null  float64
 4   gears                               21617 non-null  float64
 5   age                                 21617 non-null  float64
 6   make_model_encoded                  21617 non-null  int64  
 7   make_Fiat                           21617 non-null  int64  
 8   make_Ford                           21617 non-null  int64  
 9   make_Hyundai                        21617 non-null  int64  
 10  make_Mercedes_Benz                  21617 non-null  int64  
 11  make_Nissan                         21617 non-null  int64  
 12  make_Opel                           21617 non-null  int64  
 13  make_Peugeot                        21617 non-null  int64  
 14  make_Renault                        21617 non-null  int64  
 15  make_Seat                           21617 non-null  int64  
 16  make_Skoda                          21617 non-null  int64  
 17  make_Toyota                         21617 non-null  int64  
 18  make_Volvo                          21617 non-null  int64  
 19  location_BE                         21617 non-null  int64  
 20  location_BG                         21617 non-null  int64  
 21  location_DE                         21617 non-null  int64  
 22  location_ES                         21617 non-null  int64  
 23  location_FR                         21617 non-null  int64  
 24  location_IT                         21617 non-null  int64  
 25  location_LU                         21617 non-null  int64  
 26  location_NL                         21617 non-null  int64  
 27  body_type_Convertible               21617 non-null  int64  
 28  body_type_Coupe                     21617 non-null  int64  
 29  body_type_Off-Road/Pick-up          21617 non-null  int64  
 30  body_type_Sedan                     21617 non-null  int64  
 31  body_type_Station_wagon             21617 non-null  int64  
 32  type_Employee's_car                 21617 non-null  int64  
 33  type_Pre-registered                 21617 non-null  int64  
 34  type_Used                           21617 non-null  int64  
 35  gearbox_Manual                      21617 non-null  int64  
 36  fuel_type_Diesel                    21617 non-null  int64  
 37  fuel_type_Electric                  21617 non-null  int64  
 38  fuel_type_LPG/CNG                   21617 non-null  int64  
 39  fuel_type_Other                     21617 non-null  int64  
 40  seller_Private_seller               21617 non-null  int64  
 41  emission_class_Other                21617 non-null  int64  
 42  entertainment_media_count_Upgrated  21617 non-null  int64  
 43  engine_size_cat_Extreme             21617 non-null  int64  
 44  engine_size_cat_High                21617 non-null  int64  
 45  engine_size_cat_Low                 21617 non-null  int64  
 46  engine_size_cat_Medium              21617 non-null  int64  
 47  comfort_convenience_cat_standard    21617 non-null  int64  
dtypes: float64(5), int64(43)
memory usage: 7.9 MB

None
(21617, 48)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.86662        0.85833
mae      3312.89057     3356.44588
mse  21803984.47824 22817350.10638
rmse     4669.47368     4776.75100



HuberRegression Model Metrics :

          train_set       test_set
R2          0.86038        0.85145
mae      3228.75006     3278.13085
mse  22824013.23774 23924189.45598
rmse     4777.44840     4891.23598



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.86662        0.85833
mae      3312.89057     3356.44588
mse  21803984.47824 22817350.10638
rmse     4669.47368     4776.75100



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99987        0.87783
mae     14.02929     2863.65947
mse  21808.54950 19676078.20979
rmse   147.67718     4435.77256



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.93119        0.90364
mae      2287.87427     2606.13144
mse  11248370.79919 15518489.12648
rmse     3353.85909     3939.35136



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.70466        0.69138
mae      6045.09850     6116.04050
mse  48280654.51951 49705215.07497
rmse     6948.42820     7050.19256



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.91865        0.90485
mae      2520.98206     2638.05833
mse  13299033.38154 15324858.41518
rmse     3646.78398     3914.69774



XG Boosting Regressor Model Metrics :

         train_set      test_set
R2         0.97655       0.94007
mae     1431.37568    2066.23737
mse  3833295.54169 9651699.26309
rmse    1957.88037    3106.71841



[LightGBM] [Info] Total Bins 783
[LightGBM] [Info] Number of data points in the train set: 15131, number of used features: 46
[LightGBM] [Info] Start training from score 20942.666975



Light GBM Regressor Model Metrics :

         train_set       test_set
R2         0.95392        0.93279
mae     1921.63105     2177.07698
mse  7532223.41276 10824218.12242
rmse    2744.48965     3290.01795



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.86655        0.85820
mae      3313.15201     3357.19933
mse  21816008.50423 22837886.29711
rmse     4670.76102     4778.90011



Cat Boost Linear Ridge Model Metrics :

         train_set      test_set
R2         0.96686       0.94495
mae     1674.97405    1991.42915
mse  5418034.60978 8866152.98383
rmse    2327.66720    2977.60860



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 6045.09850 3313.15201 1674.97405 14.02929 2520.98206 3228.75006 1921.63105 3312.89057 2287.87427 3312.89057 1431.37568
MSE Score 48280654.51951 21816008.50423 5418034.60978 21808.54950 13299033.38154 22824013.23774 7532223.41276 21803984.47824 11248370.79919 21803984.47824 3833295.54169
R2 Score 0.69138 0.85820 0.94495 0.87783 0.90485 0.85145 0.93279 0.85833 0.90364 0.85833 0.94007
RMSE Score 7050.19256 4778.90011 2977.60860 4435.77256 3914.69774 4891.23598 3290.01795 4776.75100 3939.35136 4776.75100 3106.71841
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EmreDA8127's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions power_kW fuel_consumption_comb age make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 96 5 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 110 6 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 110 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 51 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   power_kW                              24529 non-null  int64
 7   fuel_consumption_comb                 24529 non-null  int64
 8   age                                   24529 non-null  int64
 9   make_model_factorized                 24529 non-null  int64
 10  body_type__Convertible                24529 non-null  int64
 11  body_type__Coupe                      24529 non-null  int64
 12  body_type__Off-Road/Pick-up           24529 non-null  int64
 13  body_type__Sedan                      24529 non-null  int64
 14  body_type__Station_wagon              24529 non-null  int64
 15  warranty_Yes                          24529 non-null  int64
 16  gearbox_Manual                        24529 non-null  int64
 17  gearbox_Semi-automatic                24529 non-null  int64
 18  fuel_type_Diesel                      24529 non-null  int64
 19  fuel_type_Electric                    24529 non-null  int64
 20  fuel_type_LPG                         24529 non-null  int64
 21  colour_Black                          24529 non-null  int64
 22  colour_Blue                           24529 non-null  int64
 23  colour_Bronze                         24529 non-null  int64
 24  colour_Brown                          24529 non-null  int64
 25  colour_Gold                           24529 non-null  int64
 26  colour_Green                          24529 non-null  int64
 27  colour_Grey                           24529 non-null  int64
 28  colour_Orange                         24529 non-null  int64
 29  colour_Red                            24529 non-null  int64
 30  colour_Silver                         24529 non-null  int64
 31  colour_Violet                         24529 non-null  int64
 32  colour_White                          24529 non-null  int64
 33  colour_Yellow                         24529 non-null  int64
 34  drivetrain_Front                      24529 non-null  int64
 35  drivetrain_Rear                       24529 non-null  int64
 36  non_smoker_Yes                        24529 non-null  int64
 37  emission_sticker_No_sticker           24529 non-null  int64
 38  emission_sticker_Red                  24529 non-null  int64
 39  emission_sticker_Yellow               24529 non-null  int64
 40  upholstery_Full_leather               24529 non-null  int64
 41  upholstery_Other                      24529 non-null  int64
 42  upholstery_Part_leather               24529 non-null  int64
 43  upholstery_Velour                     24529 non-null  int64
 44  upholstery_alcantara                  24529 non-null  int64
 45  safety_security_package_Basic         24529 non-null  int64
 46  safety_security_package_Enhanced      24529 non-null  int64
 47  comfort_convenience_package_Basic     24529 non-null  int64
 48  comfort_convenience_package_Enhanced  24529 non-null  int64
 49  ent_media_package_Basic               24529 non-null  int64
 50  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(51)
memory usage: 9.5 MB

None
(24529, 51)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



HuberRegression Model Metrics :

          train_set       test_set
R2          0.74842        0.75294
mae      4644.22247     4622.21707
mse  56492043.07455 55071902.37974
rmse     7516.11888     7421.04456



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.84257
mae     1.14543     3307.79947
mse  1328.78718 35092847.19058
rmse   36.45253     5923.92161



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92145        0.88511
mae      2565.42971     2993.96732
mse  17639509.41618 25610097.71287
rmse     4199.94160     5060.64203



Ada Boost Regressor Model Metrics :

          train_set        test_set
R2          0.55878         0.53899
mae      8411.11711      8462.59512
mse  99077157.86718 102761542.31176
rmse     9953.75094     10137.13679



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.89748        0.87252
mae      3061.44094     3244.39116
mse  23020963.52384 28416434.69317
rmse     4798.01662     5330.70677



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.97580        0.91265
mae     1657.36035     2539.00851
mse  5433075.53408 19469922.88249
rmse    2330.89587     4412.47356



[LightGBM] [Info] Total Bins 1322
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

          train_set       test_set
R2          0.94720        0.90983
mae      2283.47161     2674.31067
mse  11855808.80354 20100010.33039
rmse     3443.22651     4483.30351



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.76655        0.76659
mae      4839.14450     4850.98532
mse  52421491.42523 52028553.03376
rmse     7240.26874     7213.08208



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.96380        0.92139
mae     1976.12172     2477.60347
mse  8127895.32365 17521714.02079
rmse    2850.94639     4185.89465



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 8411.11711 4839.14450 1976.12172 1.14543 3061.44094 4644.22247 2283.47161 4841.22970 2565.42971 4841.22970 1657.36035
MSE Score 99077157.86718 52421491.42523 8127895.32365 1328.78718 23020963.52384 56492043.07455 11855808.80354 52412344.82297 17639509.41618 52412344.82297 5433075.53408
R2 Score 0.53899 0.76659 0.92139 0.84257 0.87252 0.75294 0.90983 0.76651 0.88511 0.76651 0.91265
RMSE Score 10137.13679 7213.08208 4185.89465 5923.92161 5330.70677 7421.04456 4483.30351 7214.29080 5060.64203 7214.29080 4412.47356
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EsraDA8133's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage power gears age make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other seller_Private_seller full_service_history_Yes non_smoker_vehicle_Yes emission_class_Other upholstery_Leather upholstery_Other previous_owner_Second_Hand entertainment_media_count_Upgrated engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium comfort_convenience_cat_standard
0 16950 120200.00000 75.00000 6.00000 6.00000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1
1 46990 18995.00000 225.00000 7.00000 2.00000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1
2 16800 197000.00000 100.00000 7.00000 7.00000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1
3 4690 165000.00000 90.00000 6.00000 17.00000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 0 0 1 1
4 22550 83339.00000 90.00000 7.00000 4.00000 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21614 entries, 0 to 21613
Data columns (total 39 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   price                               21614 non-null  int64  
 1   mileage                             21614 non-null  float64
 2   power                               21614 non-null  float64
 3   gears                               21614 non-null  float64
 4   age                                 21614 non-null  float64
 5   make_Fiat                           21614 non-null  int64  
 6   make_Ford                           21614 non-null  int64  
 7   make_Hyundai                        21614 non-null  int64  
 8   make_Mercedes_Benz                  21614 non-null  int64  
 9   make_Nissan                         21614 non-null  int64  
 10  make_Opel                           21614 non-null  int64  
 11  make_Peugeot                        21614 non-null  int64  
 12  make_Renault                        21614 non-null  int64  
 13  make_Seat                           21614 non-null  int64  
 14  make_Skoda                          21614 non-null  int64  
 15  make_Toyota                         21614 non-null  int64  
 16  make_Volvo                          21614 non-null  int64  
 17  body_type_Convertible               21614 non-null  int64  
 18  body_type_Coupe                     21614 non-null  int64  
 19  body_type_Off-Road/Pick-up          21614 non-null  int64  
 20  body_type_Sedan                     21614 non-null  int64  
 21  body_type_Station_wagon             21614 non-null  int64  
 22  gearbox_Manual                      21614 non-null  int64  
 23  fuel_type_Diesel                    21614 non-null  int64  
 24  fuel_type_Electric                  21614 non-null  int64  
 25  fuel_type_LPG/CNG                   21614 non-null  int64  
 26  fuel_type_Other                     21614 non-null  int64  
 27  seller_Private_seller               21614 non-null  int64  
 28  full_service_history_Yes            21614 non-null  int64  
 29  non_smoker_vehicle_Yes              21614 non-null  int64  
 30  emission_class_Other                21614 non-null  int64  
 31  upholstery_Leather                  21614 non-null  int64  
 32  upholstery_Other                    21614 non-null  int64  
 33  previous_owner_Second_Hand          21614 non-null  int64  
 34  entertainment_media_count_Upgrated  21614 non-null  int64  
 35  engine_size_cat_High                21614 non-null  int64  
 36  engine_size_cat_Low                 21614 non-null  int64  
 37  engine_size_cat_Medium              21614 non-null  int64  
 38  comfort_convenience_cat_standard    21614 non-null  int64  
dtypes: float64(4), int64(35)
memory usage: 6.4 MB

None
(21614, 39)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.85572        0.85189
mae      3439.46521     3450.23142
mse  23602120.65529 23816739.52861
rmse     4858.20138     4880.23970



HuberRegression Model Metrics :

          train_set       test_set
R2          0.84930        0.84420
mae      3333.51086     3368.34798
mse  24652134.56788 25053323.43071
rmse     4965.09160     5005.32950



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.85572        0.85189
mae      3439.46521     3450.23142
mse  23602120.65529 23816739.52861
rmse     4858.20138     4880.23970



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99980        0.85108
mae     15.39357     3135.57355
mse  32626.07141 23947007.28512
rmse   180.62688     4893.56795



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92782        0.89971
mae      2347.47044     2662.04003
mse  11807038.02992 16127153.55726
rmse     3436.13708     4015.86274



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.71887        0.70920
mae      5777.21584     5806.06945
mse  45989889.55000 46761752.32734
rmse     6781.58459     6838.25653



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.91329        0.90278
mae      2595.94631     2666.05295
mse  14184987.69031 15633793.58182
rmse     3766.29628     3953.95923



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.96847        0.92490
mae     1632.52562     2287.87443
mse  5157898.81686 12076072.58719
rmse    2271.10079     3475.06440



[LightGBM] [Info] Total Bins 557
[LightGBM] [Info] Number of data points in the train set: 15129, number of used features: 38
[LightGBM] [Info] Start training from score 20941.412387



Light GBM Regressor Model Metrics :

         train_set       test_set
R2         0.94528        0.92095
mae     2073.46370     2342.80021
mse  8950888.66248 12711159.15825
rmse    2991.80358     3565.27126



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.85570        0.85179
mae      3438.04101     3450.46944
mse  23604856.51960 23833064.07750
rmse     4858.48294     4881.91193



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.95786        0.92901
mae     1836.46852     2204.36621
mse  6893465.49282 11414997.41655
rmse    2625.54099     3378.60880



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 5777.21584 3438.04101 1836.46852 15.39357 2595.94631 3333.51086 2073.46370 3439.46521 2347.47044 3439.46521 1632.52562
MSE Score 45989889.55000 23604856.51960 6893465.49282 32626.07141 14184987.69031 24652134.56788 8950888.66248 23602120.65529 11807038.02992 23602120.65529 5157898.81686
R2 Score 0.70920 0.85179 0.92901 0.85108 0.90278 0.84420 0.92095 0.85189 0.89971 0.85189 0.92490
RMSE Score 6838.25653 4881.91193 3378.60880 4893.56795 3953.95923 5005.32950 3565.27126 4880.23970 4015.86274 4880.23970 3475.06440
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  GyulferaDA8131's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions power_kW fuel_consumption_comb age make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 96 5 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 110 6 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 110 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 51 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   power_kW                              24529 non-null  int64
 7   fuel_consumption_comb                 24529 non-null  int64
 8   age                                   24529 non-null  int64
 9   make_model_factorized                 24529 non-null  int64
 10  body_type__Convertible                24529 non-null  int64
 11  body_type__Coupe                      24529 non-null  int64
 12  body_type__Off-Road/Pick-up           24529 non-null  int64
 13  body_type__Sedan                      24529 non-null  int64
 14  body_type__Station_wagon              24529 non-null  int64
 15  warranty_Yes                          24529 non-null  int64
 16  gearbox_Manual                        24529 non-null  int64
 17  gearbox_Semi-automatic                24529 non-null  int64
 18  fuel_type_Diesel                      24529 non-null  int64
 19  fuel_type_Electric                    24529 non-null  int64
 20  fuel_type_LPG                         24529 non-null  int64
 21  colour_Black                          24529 non-null  int64
 22  colour_Blue                           24529 non-null  int64
 23  colour_Bronze                         24529 non-null  int64
 24  colour_Brown                          24529 non-null  int64
 25  colour_Gold                           24529 non-null  int64
 26  colour_Green                          24529 non-null  int64
 27  colour_Grey                           24529 non-null  int64
 28  colour_Orange                         24529 non-null  int64
 29  colour_Red                            24529 non-null  int64
 30  colour_Silver                         24529 non-null  int64
 31  colour_Violet                         24529 non-null  int64
 32  colour_White                          24529 non-null  int64
 33  colour_Yellow                         24529 non-null  int64
 34  drivetrain_Front                      24529 non-null  int64
 35  drivetrain_Rear                       24529 non-null  int64
 36  non_smoker_Yes                        24529 non-null  int64
 37  emission_sticker_No_sticker           24529 non-null  int64
 38  emission_sticker_Red                  24529 non-null  int64
 39  emission_sticker_Yellow               24529 non-null  int64
 40  upholstery_Full_leather               24529 non-null  int64
 41  upholstery_Other                      24529 non-null  int64
 42  upholstery_Part_leather               24529 non-null  int64
 43  upholstery_Velour                     24529 non-null  int64
 44  upholstery_alcantara                  24529 non-null  int64
 45  safety_security_package_Basic         24529 non-null  int64
 46  safety_security_package_Enhanced      24529 non-null  int64
 47  comfort_convenience_package_Basic     24529 non-null  int64
 48  comfort_convenience_package_Enhanced  24529 non-null  int64
 49  ent_media_package_Basic               24529 non-null  int64
 50  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(51)
memory usage: 9.5 MB

None
(24529, 51)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



HuberRegression Model Metrics :

          train_set       test_set
R2          0.74842        0.75294
mae      4644.22247     4622.21707
mse  56492043.07455 55071902.37974
rmse     7516.11888     7421.04456



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.84257
mae     1.14543     3307.79947
mse  1328.78718 35092847.19058
rmse   36.45253     5923.92161



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92145        0.88511
mae      2565.42971     2993.96732
mse  17639509.41618 25610097.71287
rmse     4199.94160     5060.64203



Ada Boost Regressor Model Metrics :

          train_set        test_set
R2          0.55878         0.53899
mae      8411.11711      8462.59512
mse  99077157.86718 102761542.31176
rmse     9953.75094     10137.13679



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.89748        0.87252
mae      3061.44094     3244.39116
mse  23020963.52384 28416434.69317
rmse     4798.01662     5330.70677



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.97580        0.91265
mae     1657.36035     2539.00851
mse  5433075.53408 19469922.88249
rmse    2330.89587     4412.47356



[LightGBM] [Info] Total Bins 1322
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

          train_set       test_set
R2          0.94720        0.90983
mae      2283.47161     2674.31067
mse  11855808.80354 20100010.33039
rmse     3443.22651     4483.30351



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.76655        0.76659
mae      4839.14450     4850.98532
mse  52421491.42523 52028553.03376
rmse     7240.26874     7213.08208



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.96380        0.92139
mae     1976.12172     2477.60347
mse  8127895.32365 17521714.02079
rmse    2850.94639     4185.89465



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 8411.11711 4839.14450 1976.12172 1.14543 3061.44094 4644.22247 2283.47161 4841.22970 2565.42971 4841.22970 1657.36035
MSE Score 99077157.86718 52421491.42523 8127895.32365 1328.78718 23020963.52384 56492043.07455 11855808.80354 52412344.82297 17639509.41618 52412344.82297 5433075.53408
R2 Score 0.53899 0.76659 0.92139 0.84257 0.87252 0.75294 0.90983 0.76651 0.88511 0.76651 0.91265
RMSE Score 10137.13679 7213.08208 4185.89465 5923.92161 5330.70677 7421.04456 4483.30351 7214.29080 5060.64203 7214.29080 4412.47356
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  HasanDA8121's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions power_kW fuel_consumption_comb age make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 96 5 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 110 6 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 110 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 51 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   power_kW                              24529 non-null  int64
 7   fuel_consumption_comb                 24529 non-null  int64
 8   age                                   24529 non-null  int64
 9   make_model_factorized                 24529 non-null  int64
 10  body_type__Convertible                24529 non-null  int64
 11  body_type__Coupe                      24529 non-null  int64
 12  body_type__Off-Road/Pick-up           24529 non-null  int64
 13  body_type__Sedan                      24529 non-null  int64
 14  body_type__Station_wagon              24529 non-null  int64
 15  warranty_Yes                          24529 non-null  int64
 16  gearbox_Manual                        24529 non-null  int64
 17  gearbox_Semi-automatic                24529 non-null  int64
 18  fuel_type_Diesel                      24529 non-null  int64
 19  fuel_type_Electric                    24529 non-null  int64
 20  fuel_type_LPG                         24529 non-null  int64
 21  colour_Black                          24529 non-null  int64
 22  colour_Blue                           24529 non-null  int64
 23  colour_Bronze                         24529 non-null  int64
 24  colour_Brown                          24529 non-null  int64
 25  colour_Gold                           24529 non-null  int64
 26  colour_Green                          24529 non-null  int64
 27  colour_Grey                           24529 non-null  int64
 28  colour_Orange                         24529 non-null  int64
 29  colour_Red                            24529 non-null  int64
 30  colour_Silver                         24529 non-null  int64
 31  colour_Violet                         24529 non-null  int64
 32  colour_White                          24529 non-null  int64
 33  colour_Yellow                         24529 non-null  int64
 34  drivetrain_Front                      24529 non-null  int64
 35  drivetrain_Rear                       24529 non-null  int64
 36  non_smoker_Yes                        24529 non-null  int64
 37  emission_sticker_No_sticker           24529 non-null  int64
 38  emission_sticker_Red                  24529 non-null  int64
 39  emission_sticker_Yellow               24529 non-null  int64
 40  upholstery_Full_leather               24529 non-null  int64
 41  upholstery_Other                      24529 non-null  int64
 42  upholstery_Part_leather               24529 non-null  int64
 43  upholstery_Velour                     24529 non-null  int64
 44  upholstery_alcantara                  24529 non-null  int64
 45  safety_security_package_Basic         24529 non-null  int64
 46  safety_security_package_Enhanced      24529 non-null  int64
 47  comfort_convenience_package_Basic     24529 non-null  int64
 48  comfort_convenience_package_Enhanced  24529 non-null  int64
 49  ent_media_package_Basic               24529 non-null  int64
 50  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(51)
memory usage: 9.5 MB

None
(24529, 51)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



HuberRegression Model Metrics :

          train_set       test_set
R2          0.74842        0.75294
mae      4644.22247     4622.21707
mse  56492043.07455 55071902.37974
rmse     7516.11888     7421.04456



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.84257
mae     1.14543     3307.79947
mse  1328.78718 35092847.19058
rmse   36.45253     5923.92161



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92145        0.88511
mae      2565.42971     2993.96732
mse  17639509.41618 25610097.71287
rmse     4199.94160     5060.64203



Ada Boost Regressor Model Metrics :

          train_set        test_set
R2          0.55878         0.53899
mae      8411.11711      8462.59512
mse  99077157.86718 102761542.31176
rmse     9953.75094     10137.13679



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.89748        0.87252
mae      3061.44094     3244.39116
mse  23020963.52384 28416434.69317
rmse     4798.01662     5330.70677



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.97580        0.91265
mae     1657.36035     2539.00851
mse  5433075.53408 19469922.88249
rmse    2330.89587     4412.47356



[LightGBM] [Info] Total Bins 1322
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

          train_set       test_set
R2          0.94720        0.90983
mae      2283.47161     2674.31067
mse  11855808.80354 20100010.33039
rmse     3443.22651     4483.30351



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.76655        0.76659
mae      4839.14450     4850.98532
mse  52421491.42523 52028553.03376
rmse     7240.26874     7213.08208



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.96380        0.92139
mae     1976.12172     2477.60347
mse  8127895.32365 17521714.02079
rmse    2850.94639     4185.89465



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 8411.11711 4839.14450 1976.12172 1.14543 3061.44094 4644.22247 2283.47161 4841.22970 2565.42971 4841.22970 1657.36035
MSE Score 99077157.86718 52421491.42523 8127895.32365 1328.78718 23020963.52384 56492043.07455 11855808.80354 52412344.82297 17639509.41618 52412344.82297 5433075.53408
R2 Score 0.53899 0.76659 0.92139 0.84257 0.87252 0.75294 0.90983 0.76651 0.88511 0.76651 0.91265
RMSE Score 10137.13679 7213.08208 4185.89465 5923.92161 5330.70677 7421.04456 4483.30351 7214.29080 5060.64203 7214.29080 4412.47356
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  NurdanDA8123's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage seats engine_size gears co2_emissions power_kW fuel_consumption_comb age make_model_factorized body_type__Convertible body_type__Coupe body_type__Off-Road/Pick-up body_type__Sedan body_type__Station_wagon warranty_Yes gearbox_Manual gearbox_Semi-automatic fuel_type_Diesel fuel_type_Electric fuel_type_LPG colour_Black colour_Blue colour_Bronze colour_Brown colour_Gold colour_Green colour_Grey colour_Orange colour_Red colour_Silver colour_Violet colour_White colour_Yellow drivetrain_Front drivetrain_Rear non_smoker_Yes emission_sticker_No_sticker emission_sticker_Red emission_sticker_Yellow upholstery_Full_leather upholstery_Other upholstery_Part_leather upholstery_Velour upholstery_alcantara safety_security_package_Basic safety_security_package_Enhanced comfort_convenience_package_Basic comfort_convenience_package_Enhanced ent_media_package_Basic ent_media_package_Enhanced
0 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0
1 21000 1000 5 1461 6 106 85 4 0 0 0 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
2 21150 4500 5 1332 6 131 96 5 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
3 25190 4018 5 1332 6 153 110 6 0 0 0 0 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 24222 8000 5 1332 6 121 110 5 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 24529 entries, 0 to 24528
Data columns (total 51 columns):
 #   Column                                Non-Null Count  Dtype
---  ------                                --------------  -----
 0   price                                 24529 non-null  int64
 1   mileage                               24529 non-null  int64
 2   seats                                 24529 non-null  int64
 3   engine_size                           24529 non-null  int64
 4   gears                                 24529 non-null  int64
 5   co2_emissions                         24529 non-null  int64
 6   power_kW                              24529 non-null  int64
 7   fuel_consumption_comb                 24529 non-null  int64
 8   age                                   24529 non-null  int64
 9   make_model_factorized                 24529 non-null  int64
 10  body_type__Convertible                24529 non-null  int64
 11  body_type__Coupe                      24529 non-null  int64
 12  body_type__Off-Road/Pick-up           24529 non-null  int64
 13  body_type__Sedan                      24529 non-null  int64
 14  body_type__Station_wagon              24529 non-null  int64
 15  warranty_Yes                          24529 non-null  int64
 16  gearbox_Manual                        24529 non-null  int64
 17  gearbox_Semi-automatic                24529 non-null  int64
 18  fuel_type_Diesel                      24529 non-null  int64
 19  fuel_type_Electric                    24529 non-null  int64
 20  fuel_type_LPG                         24529 non-null  int64
 21  colour_Black                          24529 non-null  int64
 22  colour_Blue                           24529 non-null  int64
 23  colour_Bronze                         24529 non-null  int64
 24  colour_Brown                          24529 non-null  int64
 25  colour_Gold                           24529 non-null  int64
 26  colour_Green                          24529 non-null  int64
 27  colour_Grey                           24529 non-null  int64
 28  colour_Orange                         24529 non-null  int64
 29  colour_Red                            24529 non-null  int64
 30  colour_Silver                         24529 non-null  int64
 31  colour_Violet                         24529 non-null  int64
 32  colour_White                          24529 non-null  int64
 33  colour_Yellow                         24529 non-null  int64
 34  drivetrain_Front                      24529 non-null  int64
 35  drivetrain_Rear                       24529 non-null  int64
 36  non_smoker_Yes                        24529 non-null  int64
 37  emission_sticker_No_sticker           24529 non-null  int64
 38  emission_sticker_Red                  24529 non-null  int64
 39  emission_sticker_Yellow               24529 non-null  int64
 40  upholstery_Full_leather               24529 non-null  int64
 41  upholstery_Other                      24529 non-null  int64
 42  upholstery_Part_leather               24529 non-null  int64
 43  upholstery_Velour                     24529 non-null  int64
 44  upholstery_alcantara                  24529 non-null  int64
 45  safety_security_package_Basic         24529 non-null  int64
 46  safety_security_package_Enhanced      24529 non-null  int64
 47  comfort_convenience_package_Basic     24529 non-null  int64
 48  comfort_convenience_package_Enhanced  24529 non-null  int64
 49  ent_media_package_Basic               24529 non-null  int64
 50  ent_media_package_Enhanced            24529 non-null  int64
dtypes: int64(51)
memory usage: 9.5 MB

None
(24529, 51)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



HuberRegression Model Metrics :

          train_set       test_set
R2          0.74842        0.75294
mae      4644.22247     4622.21707
mse  56492043.07455 55071902.37974
rmse     7516.11888     7421.04456



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.76659        0.76651
mae      4841.22970     4854.47431
mse  52412344.82297 52045991.71109
rmse     7239.63706     7214.29080



Decision Tree Regressor Model Metrics :

      train_set       test_set
R2      0.99999        0.84257
mae     1.14543     3307.79947
mse  1328.78718 35092847.19058
rmse   36.45253     5923.92161



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92145        0.88511
mae      2565.42971     2993.96732
mse  17639509.41618 25610097.71287
rmse     4199.94160     5060.64203



Ada Boost Regressor Model Metrics :

          train_set        test_set
R2          0.55878         0.53899
mae      8411.11711      8462.59512
mse  99077157.86718 102761542.31176
rmse     9953.75094     10137.13679



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.89748        0.87252
mae      3061.44094     3244.39116
mse  23020963.52384 28416434.69317
rmse     4798.01662     5330.70677



XG Boosting Regressor Model Metrics :

         train_set       test_set
R2         0.97580        0.91265
mae     1657.36035     2539.00851
mse  5433075.53408 19469922.88249
rmse    2330.89587     4412.47356



[LightGBM] [Info] Total Bins 1322
[LightGBM] [Info] Number of data points in the train set: 17170, number of used features: 50
[LightGBM] [Info] Start training from score 21429.045836



Light GBM Regressor Model Metrics :

          train_set       test_set
R2          0.94720        0.90983
mae      2283.47161     2674.31067
mse  11855808.80354 20100010.33039
rmse     3443.22651     4483.30351



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.76655        0.76659
mae      4839.14450     4850.98532
mse  52421491.42523 52028553.03376
rmse     7240.26874     7213.08208



Cat Boost Linear Ridge Model Metrics :

         train_set       test_set
R2         0.96380        0.92139
mae     1976.12172     2477.60347
mse  8127895.32365 17521714.02079
rmse    2850.94639     4185.89465



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 8411.11711 4839.14450 1976.12172 1.14543 3061.44094 4644.22247 2283.47161 4841.22970 2565.42971 4841.22970 1657.36035
MSE Score 99077157.86718 52421491.42523 8127895.32365 1328.78718 23020963.52384 56492043.07455 11855808.80354 52412344.82297 17639509.41618 52412344.82297 5433075.53408
R2 Score 0.53899 0.76659 0.92139 0.84257 0.87252 0.75294 0.90983 0.76651 0.88511 0.76651 0.91265
RMSE Score 10137.13679 7213.08208 4185.89465 5923.92161 5330.70677 7421.04456 4483.30351 7214.29080 5060.64203 7214.29080 4412.47356
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  SerahsiDA8135's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage power gears age make_model_encoded make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other seller_Private_seller drivetrain_Front drivetrain_Rear emission_class_Other previous_owner_Second_Hand engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium comfort_convenience_cat_standard
0 16950 120200.00000 75.00000 6.00000 6.00000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 0 1 1
1 46990 18995.00000 225.00000 7.00000 2.00000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1
2 16800 197000.00000 100.00000 7.00000 7.00000 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 1
3 4690 165000.00000 90.00000 6.00000 17.00000 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1
4 22550 83339.00000 90.00000 7.00000 4.00000 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21595 entries, 0 to 21594
Data columns (total 37 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   price                             21595 non-null  int64  
 1   mileage                           21595 non-null  float64
 2   power                             21595 non-null  float64
 3   gears                             21595 non-null  float64
 4   age                               21595 non-null  float64
 5   make_model_encoded                21595 non-null  int64  
 6   make_Fiat                         21595 non-null  int64  
 7   make_Ford                         21595 non-null  int64  
 8   make_Hyundai                      21595 non-null  int64  
 9   make_Mercedes_Benz                21595 non-null  int64  
 10  make_Nissan                       21595 non-null  int64  
 11  make_Opel                         21595 non-null  int64  
 12  make_Peugeot                      21595 non-null  int64  
 13  make_Renault                      21595 non-null  int64  
 14  make_Seat                         21595 non-null  int64  
 15  make_Skoda                        21595 non-null  int64  
 16  make_Toyota                       21595 non-null  int64  
 17  make_Volvo                        21595 non-null  int64  
 18  body_type_Convertible             21595 non-null  int64  
 19  body_type_Coupe                   21595 non-null  int64  
 20  body_type_Off-Road/Pick-up        21595 non-null  int64  
 21  body_type_Sedan                   21595 non-null  int64  
 22  body_type_Station_wagon           21595 non-null  int64  
 23  gearbox_Manual                    21595 non-null  int64  
 24  fuel_type_Diesel                  21595 non-null  int64  
 25  fuel_type_Electric                21595 non-null  int64  
 26  fuel_type_LPG/CNG                 21595 non-null  int64  
 27  fuel_type_Other                   21595 non-null  int64  
 28  seller_Private_seller             21595 non-null  int64  
 29  drivetrain_Front                  21595 non-null  int64  
 30  drivetrain_Rear                   21595 non-null  int64  
 31  emission_class_Other              21595 non-null  int64  
 32  previous_owner_Second_Hand        21595 non-null  int64  
 33  engine_size_cat_High              21595 non-null  int64  
 34  engine_size_cat_Low               21595 non-null  int64  
 35  engine_size_cat_Medium            21595 non-null  int64  
 36  comfort_convenience_cat_standard  21595 non-null  int64  
dtypes: float64(4), int64(33)
memory usage: 6.1 MB

None
(21595, 37)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.85925        0.85440
mae      3402.34857     3417.53166
mse  22872699.57840 23820881.22452
rmse     4782.54112     4880.66401



HuberRegression Model Metrics :

          train_set       test_set
R2          0.85398        0.84773
mae      3317.66856     3349.87957
mse  23729535.09775 24912326.56239
rmse     4871.29707     4991.22496



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.85925        0.85440
mae      3402.34857     3417.53166
mse  22872699.57840 23820881.22452
rmse     4782.54112     4880.66401



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99981        0.87368
mae     18.10270     2911.60305
mse  31387.70910 20667313.64821
rmse   177.16577     4546.13172



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.93185        0.90941
mae      2263.07513     2554.28258
mse  11075821.10222 14820676.25809
rmse     3328.03562     3849.76314



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.68922        0.68685
mae      6211.44093     6220.87469
mse  50505573.19470 51234335.79702
rmse     7106.72732     7157.81641



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.91853        0.90693
mae      2521.91759     2644.81616
mse  13239480.89893 15226621.74067
rmse     3638.60975     3902.13041



XG Boosting Regressor Model Metrics :

         train_set      test_set
R2         0.97450       0.94083
mae     1494.12929    2086.18831
mse  4144312.27589 9680973.95705
rmse    2035.75840    3111.42635



[LightGBM] [Info] Total Bins 768
[LightGBM] [Info] Number of data points in the train set: 15116, number of used features: 36
[LightGBM] [Info] Start training from score 20967.344205



Light GBM Regressor Model Metrics :

         train_set       test_set
R2         0.95113        0.93218
mae     1962.72444     2210.20362
mse  7941155.28711 11096360.37160
rmse    2818.00555     3331.11999



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.85922        0.85421
mae      3402.85400     3419.05094
mse  22878997.86064 23853156.81437
rmse     4783.19954     4883.96937



Cat Boost Linear Ridge Model Metrics :

         train_set      test_set
R2         0.96417       0.94335
mae     1738.53207    2039.59588
mse  5821979.87276 9268519.37805
rmse    2412.87792    3044.42431



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 6211.44093 3402.85400 1738.53207 18.10270 2521.91759 3317.66856 1962.72444 3402.34857 2263.07513 3402.34857 1494.12929
MSE Score 50505573.19470 22878997.86064 5821979.87276 31387.70910 13239480.89893 23729535.09775 7941155.28711 22872699.57840 11075821.10222 22872699.57840 4144312.27589
R2 Score 0.68685 0.85421 0.94335 0.87368 0.90693 0.84773 0.93218 0.85440 0.90941 0.85440 0.94083
RMSE Score 7157.81641 4883.96937 3044.42431 4546.13172 3902.13041 4991.22496 3331.11999 4880.66401 3849.76314 4880.66401 3111.42635
No description has been provided for this image 


<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  SezerDA8134's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
price mileage power gears age make_model_encoded make_Fiat make_Ford make_Hyundai make_Mercedes_Benz make_Nissan make_Opel make_Peugeot make_Renault make_Seat make_Skoda make_Toyota make_Volvo location_BE location_BG location_DE location_ES location_FR location_IT location_LU location_NL body_type_Convertible body_type_Coupe body_type_Off-Road/Pick-up body_type_Sedan body_type_Station_wagon gearbox_Manual fuel_type_Diesel fuel_type_Electric fuel_type_LPG/CNG fuel_type_Other seller_Private_seller drivetrain_Front drivetrain_Rear emission_class_Euro_2 emission_class_Euro_3 emission_class_Euro_4 emission_class_Euro_5 emission_class_Euro_6 upholstery_Leather upholstery_Other previous_owner_Second_Hand entertainment_media_count_Upgrated engine_size_cat_High engine_size_cat_Low engine_size_cat_Medium safety_security_category_Middle safety_security_category_Premium comfort_convenience_cat_standard
0 16950 120200.00000 75.00000 6.00000 6.00000 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1
1 46990 18995.00000 225.00000 7.00000 2.00000 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 1
2 16800 197000.00000 100.00000 7.00000 7.00000 2 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 1 1
3 4690 165000.00000 90.00000 6.00000 17.00000 3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1
4 22550 83339.00000 90.00000 7.00000 4.00000 4 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 1 0 1 0 
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 20194 entries, 0 to 20193
Data columns (total 54 columns):
 #   Column                              Non-Null Count  Dtype  
---  ------                              --------------  -----  
 0   price                               20194 non-null  int64  
 1   mileage                             20194 non-null  float64
 2   power                               20194 non-null  float64
 3   gears                               20194 non-null  float64
 4   age                                 20194 non-null  float64
 5   make_model_encoded                  20194 non-null  int64  
 6   make_Fiat                           20194 non-null  int64  
 7   make_Ford                           20194 non-null  int64  
 8   make_Hyundai                        20194 non-null  int64  
 9   make_Mercedes_Benz                  20194 non-null  int64  
 10  make_Nissan                         20194 non-null  int64  
 11  make_Opel                           20194 non-null  int64  
 12  make_Peugeot                        20194 non-null  int64  
 13  make_Renault                        20194 non-null  int64  
 14  make_Seat                           20194 non-null  int64  
 15  make_Skoda                          20194 non-null  int64  
 16  make_Toyota                         20194 non-null  int64  
 17  make_Volvo                          20194 non-null  int64  
 18  location_BE                         20194 non-null  int64  
 19  location_BG                         20194 non-null  int64  
 20  location_DE                         20194 non-null  int64  
 21  location_ES                         20194 non-null  int64  
 22  location_FR                         20194 non-null  int64  
 23  location_IT                         20194 non-null  int64  
 24  location_LU                         20194 non-null  int64  
 25  location_NL                         20194 non-null  int64  
 26  body_type_Convertible               20194 non-null  int64  
 27  body_type_Coupe                     20194 non-null  int64  
 28  body_type_Off-Road/Pick-up          20194 non-null  int64  
 29  body_type_Sedan                     20194 non-null  int64  
 30  body_type_Station_wagon             20194 non-null  int64  
 31  gearbox_Manual                      20194 non-null  int64  
 32  fuel_type_Diesel                    20194 non-null  int64  
 33  fuel_type_Electric                  20194 non-null  int64  
 34  fuel_type_LPG/CNG                   20194 non-null  int64  
 35  fuel_type_Other                     20194 non-null  int64  
 36  seller_Private_seller               20194 non-null  int64  
 37  drivetrain_Front                    20194 non-null  int64  
 38  drivetrain_Rear                     20194 non-null  int64  
 39  emission_class_Euro_2               20194 non-null  int64  
 40  emission_class_Euro_3               20194 non-null  int64  
 41  emission_class_Euro_4               20194 non-null  int64  
 42  emission_class_Euro_5               20194 non-null  int64  
 43  emission_class_Euro_6               20194 non-null  int64  
 44  upholstery_Leather                  20194 non-null  int64  
 45  upholstery_Other                    20194 non-null  int64  
 46  previous_owner_Second_Hand          20194 non-null  int64  
 47  entertainment_media_count_Upgrated  20194 non-null  int64  
 48  engine_size_cat_High                20194 non-null  int64  
 49  engine_size_cat_Low                 20194 non-null  int64  
 50  engine_size_cat_Medium              20194 non-null  int64  
 51  safety_security_category_Middle     20194 non-null  int64  
 52  safety_security_category_Premium    20194 non-null  int64  
 53  comfort_convenience_cat_standard    20194 non-null  int64  
dtypes: float64(4), int64(50)
memory usage: 8.3 MB

None
(20194, 54)
There is no null value
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<   MODEL SCORES    >>>>>>>>>>>>>>>>>>>>>>>>>>>>>

Linear Model Metrics :

          train_set       test_set
R2          0.86627        0.86246
mae      3081.78410     3143.04206
mse  19201743.31654 19906149.33951
rmse     4381.97938     4461.63079



HuberRegression Model Metrics :

          train_set       test_set
R2          0.86050        0.85630
mae      2996.86822     3066.55568
mse  20030121.31785 20797124.18570
rmse     4475.50235     4560.38641



WLS (Weighted Least Squares) Model Metrics :

          train_set       test_set
R2          0.86627        0.86246
mae      3081.78410     3143.04206
mse  19201743.31654 19906149.33951
rmse     4381.97938     4461.63079



Decision Tree Regressor Model Metrics :

       train_set       test_set
R2       0.99990        0.86469
mae     10.95616     2818.92312
mse  14508.61453 19582166.26063
rmse   120.45171     4425.17415



Random Forest Regressor Model Metrics :

          train_set       test_set
R2          0.92951        0.90706
mae      2164.77986     2467.61313
mse  10121751.41782 13451121.12256
rmse     3181.47001     3667.57701



Ada Boost Regressor Model Metrics :

          train_set       test_set
R2          0.68048        0.67711
mae      5874.90564     5863.01689
mse  45877247.74444 46729745.70760
rmse     6773.27452     6835.91586



Gradient Boost Regressor Model Metrics :

          train_set       test_set
R2          0.91858        0.90900
mae      2369.32179     2476.80098
mse  11690941.55805 13170387.55993
rmse     3419.20189     3629.10286



XG Boosting Regressor Model Metrics :

         train_set      test_set
R2         0.97727       0.94133
mae     1324.08738    1949.70763
mse  3262950.45071 8491681.87739
rmse    1806.36388    2914.04905



[LightGBM] [Info] Total Bins 769
[LightGBM] [Info] Number of data points in the train set: 14135, number of used features: 50
[LightGBM] [Info] Start training from score 20091.370994



Light GBM Regressor Model Metrics :

         train_set      test_set
R2         0.95433       0.93540
mae     1795.69834    2071.96371
mse  6557499.77470 9348762.89768
rmse    2560.76156    3057.57468



Bayesian Linear Ridge Model Metrics :

          train_set       test_set
R2          0.86617        0.86240
mae      3082.96580     3144.31155
mse  19215087.50191 19913925.70481
rmse     4383.50174     4462.50218



Cat Boost Linear Ridge Model Metrics :

         train_set      test_set
R2         0.96820       0.94654
mae     1543.28089    1878.72040
mse  4565293.85719 7737690.92461
rmse    2136.65483    2781.67053



<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<    MODEL PERFORMANCE / COMPARISION    >>>>>>>>>>>>>>>>>>>>>>>>>>>
Supervised Models ADABoost Bayesian Ridge CatBoost Decision Tree Gradient Boosting Huber Regression LightGBM Linear Regression Random Forest Weighted Least Squares XGBoost
MAE Score 5874.90564 3082.96580 1543.28089 10.95616 2369.32179 2996.86822 1795.69834 3081.78410 2164.77986 3081.78410 1324.08738
MSE Score 45877247.74444 19215087.50191 4565293.85719 14508.61453 11690941.55805 20030121.31785 6557499.77470 19201743.31654 10121751.41782 19201743.31654 3262950.45071
R2 Score 0.67711 0.86240 0.94654 0.86469 0.90900 0.85630 0.93540 0.86246 0.90706 0.86246 0.94133
RMSE Score 6835.91586 4462.50218 2781.67053 4425.17415 3629.10286 4560.38641 3057.57468 4461.63079 3667.57701 4461.63079 2914.04905
No description has been provided for this image

Model Training by Pipelines:

ΒΆ

<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  TugceDA8122's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 808
[LightGBM] [Info] Number of data points in the train set: 17281, number of used features: 35
[LightGBM] [Info] Start training from score 20969.221168
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.85460 3430.44768 23025314.77964 4798.47005 0.85572 0.85460
1 Decision Tree Regressor 0.87928 2862.54220 19117210.83807 4372.32328 0.99981 0.87928
2 Random Forest Regressor 0.93257 2135.39459 10678858.12727 3267.85222 0.99094 0.93257
3 AdaBoost Regressor 0.64743 6513.42438 55833400.06720 7472.17506 0.66652 0.64743
4 Gradient Boosting Regressor 0.90798 2607.45976 14572142.13428 3817.34753 0.91474 0.90798
5 XGB Regressor 0.94044 2063.19448 9432105.17867 3071.17326 0.97344 0.94044
6 LGBM Regressor 0.93472 2183.33711 10338079.24885 3215.28836 0.94938 0.93472
7 Bayesian Ridge 0.85455 3431.01707 23033629.26358 4799.33634 0.85571 0.85455
8 CatBoost Regressor 0.94290 2028.32026 9041531.17330 3006.91390 0.96259 0.94290
9 SVM Regressor 0.01729 9062.79313 155620640.85701 12474.80023 0.01429 0.01729
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 6513.42438 3431.01707 2028.32026 2862.54220 2607.45976 2183.33711 3430.44768 2135.39459 9062.79313 2063.19448
MSE Score 55833400.06720 23033629.26358 9041531.17330 19117210.83807 14572142.13428 10338079.24885 23025314.77964 10678858.12727 155620640.85701 9432105.17867
R2 Score 0.64743 0.85455 0.94290 0.87928 0.90798 0.93472 0.85460 0.93257 0.01729 0.94044
RMSE Score 7472.17506 4799.33634 3006.91390 4372.32328 3817.34753 3215.28836 4798.47005 3267.85222 12474.80023 3071.17326
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  TugceDA8122's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1137
[LightGBM] [Info] Number of data points in the train set: 16640, number of used features: 61
[LightGBM] [Info] Start training from score 20502.329087
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.87218 3045.40187 19043295.54805 4363.86246 0.87682 0.87218
1 Decision Tree Regressor 0.86923 2798.95658 19482718.60394 4413.92327 0.99995 0.86923
2 Random Forest Regressor 0.93633 1972.68535 9486099.04041 3079.95114 0.99142 0.93633
3 AdaBoost Regressor 0.72096 5597.94787 41571284.24741 6447.57972 0.72739 0.72096
4 Gradient Boosting Regressor 0.91382 2422.85674 12839518.55046 3583.22739 0.92530 0.91382
5 XGB Regressor 0.94150 1918.17226 8715621.42349 2952.22313 0.97911 0.94150
6 LGBM Regressor 0.93890 1986.63867 9103296.99098 3017.16705 0.95869 0.93890
7 Bayesian Ridge 0.87210 3046.42342 19054650.70839 4365.16331 0.87679 0.87210
8 CatBoost Regressor 0.94934 1805.61014 7547825.27947 2747.33057 0.97147 0.94934
9 SVM Regressor 0.00359 8916.61725 148444628.79986 12183.78549 0.00434 0.00359
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 5597.94787 3046.42342 1805.61014 2798.95658 2422.85674 1986.63867 3045.40187 1972.68535 8916.61725 1918.17226
MSE Score 41571284.24741 19054650.70839 7547825.27947 19482718.60394 12839518.55046 9103296.99098 19043295.54805 9486099.04041 148444628.79986 8715621.42349
R2 Score 0.72096 0.87210 0.94934 0.86923 0.91382 0.93890 0.87218 0.93633 0.00359 0.94150
RMSE Score 6447.57972 4365.16331 2747.33057 4413.92327 3583.22739 3017.16705 4363.86246 3079.95114 12183.78549 2952.22313
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  AsliDA8115's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.76535 4847.86414 52491299.42002 7245.08795 0.76709 0.76535
1 Decision Tree Regressor 0.83591 3343.81618 36705363.21993 6058.49513 0.99999 0.83591
2 Random Forest Regressor 0.92269 2386.58850 17293597.15240 4158.55710 0.98841 0.92269
3 AdaBoost Regressor 0.48863 9345.22956 114391814.98679 10695.41093 0.48707 0.48863
4 Gradient Boosting Regressor 0.88034 3191.59254 26767061.30581 5173.68933 0.89266 0.88034
5 XGB Regressor 0.91894 2516.52315 18132091.42636 4258.17936 0.97550 0.91894
6 LGBM Regressor 0.91622 2650.53939 18741923.55600 4329.19433 0.94453 0.91622
7 Bayesian Ridge 0.76532 4846.54654 52498176.46425 7245.56254 0.76708 0.76532
8 CatBoost Regressor 0.92820 2422.45516 16060754.07656 4007.58706 0.96302 0.92820
9 SVM Regressor -0.02998 10299.77303 230403540.97345 15179.04941 -0.02267 -0.02998
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 9345.22956 4846.54654 2422.45516 3343.81618 3191.59254 2650.53939 4847.86414 2386.58850 10299.77303 2516.52315
MSE Score 114391814.98679 52498176.46425 16060754.07656 36705363.21993 26767061.30581 18741923.55600 52491299.42002 17293597.15240 230403540.97345 18132091.42636
R2 Score 0.48863 0.76532 0.92820 0.83591 0.88034 0.91622 0.76535 0.92269 -0.02998 0.91894
RMSE Score 10695.41093 7245.56254 4007.58706 6058.49513 5173.68933 4329.19433 7245.08795 4158.55710 15179.04941 4258.17936
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  AysegulDA8116's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

!!!!!!!!AysegulDA8116's DataFrame has non-numeric value !!!!!!!!!!

<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  DamlaDA8120's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1503
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 63
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.87947 3842.81604 26962198.98562 5192.51374 0.87789 0.87947
1 Decision Tree Regressor 0.90444 2862.71722 21376495.90289 4623.47228 0.99999 0.90444
2 Random Forest Regressor 0.95703 2019.01618 9612429.43105 3100.39182 0.99343 0.95703
3 AdaBoost Regressor 0.82715 5210.22303 38665305.06620 6218.14322 0.82302 0.82715
4 Gradient Boosting Regressor 0.93477 2683.24893 14590916.16058 3819.80578 0.93713 0.93477
5 XGB Regressor 0.95583 2113.45031 9881606.73771 3143.50230 0.98095 0.95583
6 LGBM Regressor 0.95344 2234.08197 10415015.87701 3227.23037 0.96279 0.95344
7 Bayesian Ridge 0.87943 3842.85125 26971506.82387 5193.40994 0.87789 0.87943
8 CatBoost Regressor 0.95770 2091.43829 9461908.46833 3076.02153 0.97327 0.95770
9 SVM Regressor -0.03420 10316.35794 231347282.93949 15210.10463 -0.02653 -0.03420
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 5210.22303 3842.85125 2091.43829 2862.71722 2683.24893 2234.08197 3842.81604 2019.01618 10316.35794 2113.45031
MSE Score 38665305.06620 26971506.82387 9461908.46833 21376495.90289 14590916.16058 10415015.87701 26962198.98562 9612429.43105 231347282.93949 9881606.73771
R2 Score 0.82715 0.87943 0.95770 0.90444 0.93477 0.95344 0.87947 0.95703 -0.03420 0.95583
RMSE Score 6218.14322 5193.40994 3076.02153 4623.47228 3819.80578 3227.23037 5192.51374 3100.39182 15210.10463 3143.50230
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EmreDA8119's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 835
[LightGBM] [Info] Number of data points in the train set: 17293, number of used features: 45
[LightGBM] [Info] Start training from score 20940.150061
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.86116 3368.80822 22120132.79164 4703.20452 0.86497 0.86116
1 Decision Tree Regressor 0.88210 2812.94500 18783045.11448 4333.94106 0.99985 0.88210
2 Random Forest Regressor 0.93559 2102.70655 10261299.97685 3203.32639 0.99132 0.93559
3 AdaBoost Regressor 0.65439 6409.97950 55063371.01812 7420.46973 0.66772 0.65439
4 Gradient Boosting Regressor 0.90597 2650.65952 14980440.26891 3870.45737 0.91606 0.90597
5 XGB Regressor 0.94387 2028.04336 8943210.14555 2990.52005 0.97562 0.94387
6 LGBM Regressor 0.93519 2187.83743 10326205.98676 3213.44146 0.95194 0.93519
7 Bayesian Ridge 0.86113 3368.29641 22124596.02071 4703.67899 0.86495 0.86113
8 CatBoost Regressor 0.94717 1975.71096 8417152.36143 2901.23290 0.96574 0.94717
9 SVM Regressor 0.00609 9139.59998 158349556.07574 12583.70200 0.00693 0.00609
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 6409.97950 3368.29641 1975.71096 2812.94500 2650.65952 2187.83743 3368.80822 2102.70655 9139.59998 2028.04336
MSE Score 55063371.01812 22124596.02071 8417152.36143 18783045.11448 14980440.26891 10326205.98676 22120132.79164 10261299.97685 158349556.07574 8943210.14555
R2 Score 0.65439 0.86113 0.94717 0.88210 0.90597 0.93519 0.86116 0.93559 0.00609 0.94387
RMSE Score 7420.46973 4703.67899 2901.23290 4333.94106 3870.45737 3213.44146 4703.20452 3203.32639 12583.70200 2990.52005
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EmreDA8127's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.76535 4847.86414 52491299.42002 7245.08795 0.76709 0.76535
1 Decision Tree Regressor 0.83256 3366.74759 37456168.81435 6120.14451 0.99999 0.83256
2 Random Forest Regressor 0.92246 2381.75686 17344517.50580 4164.67496 0.98845 0.92246
3 AdaBoost Regressor 0.51817 8934.71561 107784054.58741 10381.90997 0.51803 0.51817
4 Gradient Boosting Regressor 0.88066 3190.42327 26695048.58120 5166.72513 0.89266 0.88066
5 XGB Regressor 0.91894 2516.52315 18132091.42636 4258.17936 0.97550 0.91894
6 LGBM Regressor 0.91622 2650.53939 18741923.55600 4329.19433 0.94453 0.91622
7 Bayesian Ridge 0.76532 4846.54654 52498176.46425 7245.56254 0.76708 0.76532
8 CatBoost Regressor 0.92820 2422.45516 16060754.07656 4007.58706 0.96302 0.92820
9 SVM Regressor -0.02998 10299.77303 230403540.97345 15179.04941 -0.02267 -0.02998
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 8934.71561 4846.54654 2422.45516 3366.74759 3190.42327 2650.53939 4847.86414 2381.75686 10299.77303 2516.52315
MSE Score 107784054.58741 52498176.46425 16060754.07656 37456168.81435 26695048.58120 18741923.55600 52491299.42002 17344517.50580 230403540.97345 18132091.42636
R2 Score 0.51817 0.76532 0.92820 0.83256 0.88066 0.91622 0.76535 0.92246 -0.02998 0.91894
RMSE Score 10381.90997 7245.56254 4007.58706 6120.14451 5166.72513 4329.19433 7245.08795 4164.67496 15179.04941 4258.17936
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  EsraDA8133's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 599
[LightGBM] [Info] Number of data points in the train set: 17291, number of used features: 38
[LightGBM] [Info] Start training from score 20996.104505
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.85939 3383.26925 22270795.24241 4719.19434 0.85347 0.85939
1 Decision Tree Regressor 0.86805 2998.04781 20898706.10578 4571.51027 0.99979 0.86805
2 Random Forest Regressor 0.93022 2213.16567 11052513.76319 3324.53211 0.98984 0.93022
3 AdaBoost Regressor 0.63631 6662.76164 57605047.00101 7589.79888 0.65450 0.63631
4 Gradient Boosting Regressor 0.91145 2594.21352 14024854.84364 3744.97728 0.91121 0.91145
5 XGB Regressor 0.93139 2236.12026 10867216.01730 3296.54607 0.96761 0.93139
6 LGBM Regressor 0.93045 2282.51993 11016419.55072 3319.09921 0.94226 0.93045
7 Bayesian Ridge 0.85939 3382.46532 22270593.35424 4719.17295 0.85347 0.85939
8 CatBoost Regressor 0.93996 2119.18171 9509935.40723 3083.81832 0.95622 0.93996
9 SVM Regressor 0.02034 9144.93910 155167703.06966 12456.63289 0.01222 0.02034
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 6662.76164 3382.46532 2119.18171 2998.04781 2594.21352 2282.51993 3383.26925 2213.16567 9144.93910 2236.12026
MSE Score 57605047.00101 22270593.35424 9509935.40723 20898706.10578 14024854.84364 11016419.55072 22270795.24241 11052513.76319 155167703.06966 10867216.01730
R2 Score 0.63631 0.85939 0.93996 0.86805 0.91145 0.93045 0.85939 0.93022 0.02034 0.93139
RMSE Score 7589.79888 4719.17295 3083.81832 4571.51027 3744.97728 3319.09921 4719.19434 3324.53211 12456.63289 3296.54607
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  GyulferaDA8131's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.76535 4847.86414 52491299.42002 7245.08795 0.76709 0.76535
1 Decision Tree Regressor 0.83980 3332.97255 35836587.50926 5986.36680 0.99999 0.83980
2 Random Forest Regressor 0.92144 2393.00096 17572866.16310 4192.00026 0.98859 0.92144
3 AdaBoost Regressor 0.49972 9137.91688 111910030.33520 10578.75372 0.50243 0.49972
4 Gradient Boosting Regressor 0.88050 3190.66494 26731434.60854 5170.24512 0.89266 0.88050
5 XGB Regressor 0.91894 2516.52315 18132091.42636 4258.17936 0.97550 0.91894
6 LGBM Regressor 0.91622 2650.53939 18741923.55600 4329.19433 0.94453 0.91622
7 Bayesian Ridge 0.76532 4846.54654 52498176.46425 7245.56254 0.76708 0.76532
8 CatBoost Regressor 0.92820 2422.45516 16060754.07656 4007.58706 0.96302 0.92820
9 SVM Regressor -0.02998 10299.77303 230403540.97345 15179.04941 -0.02267 -0.02998
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 9137.91688 4846.54654 2422.45516 3332.97255 3190.66494 2650.53939 4847.86414 2393.00096 10299.77303 2516.52315
MSE Score 111910030.33520 52498176.46425 16060754.07656 35836587.50926 26731434.60854 18741923.55600 52491299.42002 17572866.16310 230403540.97345 18132091.42636
R2 Score 0.49972 0.76532 0.92820 0.83980 0.88050 0.91622 0.76535 0.92144 -0.02998 0.91894
RMSE Score 10578.75372 7245.56254 4007.58706 5986.36680 5170.24512 4329.19433 7245.08795 4192.00026 15179.04941 4258.17936
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  HasanDA8121's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.76535 4847.86414 52491299.42002 7245.08795 0.76709 0.76535
1 Decision Tree Regressor 0.83681 3359.63793 36506034.45225 6042.02238 0.99999 0.83681
2 Random Forest Regressor 0.92295 2374.92717 17236512.51483 4151.68791 0.98883 0.92295
3 AdaBoost Regressor 0.54249 8666.37208 102344535.27988 10116.54760 0.54292 0.54249
4 Gradient Boosting Regressor 0.88075 3189.91670 26676135.36178 5164.89452 0.89266 0.88075
5 XGB Regressor 0.91894 2516.52315 18132091.42636 4258.17936 0.97550 0.91894
6 LGBM Regressor 0.91622 2650.53939 18741923.55600 4329.19433 0.94453 0.91622
7 Bayesian Ridge 0.76532 4846.54654 52498176.46425 7245.56254 0.76708 0.76532
8 CatBoost Regressor 0.92820 2422.45516 16060754.07656 4007.58706 0.96302 0.92820
9 SVM Regressor -0.02998 10299.77303 230403540.97345 15179.04941 -0.02267 -0.02998
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 8666.37208 4846.54654 2422.45516 3359.63793 3189.91670 2650.53939 4847.86414 2374.92717 10299.77303 2516.52315
MSE Score 102344535.27988 52498176.46425 16060754.07656 36506034.45225 26676135.36178 18741923.55600 52491299.42002 17236512.51483 230403540.97345 18132091.42636
R2 Score 0.54249 0.76532 0.92820 0.83681 0.88075 0.91622 0.76535 0.92295 -0.02998 0.91894
RMSE Score 10116.54760 7245.56254 4007.58706 6042.02238 5164.89452 4329.19433 7245.08795 4151.68791 15179.04941 4258.17936
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  NurdanDA8123's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 1439
[LightGBM] [Info] Number of data points in the train set: 19623, number of used features: 50
[LightGBM] [Info] Start training from score 21426.436783
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.76535 4847.86414 52491299.42002 7245.08795 0.76709 0.76535
1 Decision Tree Regressor 0.83390 3345.34261 37156805.94409 6095.63827 0.99999 0.83390
2 Random Forest Regressor 0.92284 2368.67530 17259349.84446 4154.43737 0.98859 0.92284
3 AdaBoost Regressor 0.49683 9269.38755 112558172.12103 10609.34362 0.49537 0.49683
4 Gradient Boosting Regressor 0.88051 3190.60209 26728942.82915 5170.00414 0.89266 0.88051
5 XGB Regressor 0.91894 2516.52315 18132091.42636 4258.17936 0.97550 0.91894
6 LGBM Regressor 0.91622 2650.53939 18741923.55600 4329.19433 0.94453 0.91622
7 Bayesian Ridge 0.76532 4846.54654 52498176.46425 7245.56254 0.76708 0.76532
8 CatBoost Regressor 0.92820 2422.45516 16060754.07656 4007.58706 0.96302 0.92820
9 SVM Regressor -0.02998 10299.77303 230403540.97345 15179.04941 -0.02267 -0.02998
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 9269.38755 4846.54654 2422.45516 3345.34261 3190.60209 2650.53939 4847.86414 2368.67530 10299.77303 2516.52315
MSE Score 112558172.12103 52498176.46425 16060754.07656 37156805.94409 26728942.82915 18741923.55600 52491299.42002 17259349.84446 230403540.97345 18132091.42636
R2 Score 0.49683 0.76532 0.92820 0.83390 0.88051 0.91622 0.76535 0.92284 -0.02998 0.91894
RMSE Score 10609.34362 7245.56254 4007.58706 6095.63827 5170.00414 4329.19433 7245.08795 4154.43737 15179.04941 4258.17936
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  SerahsiDA8135's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 810
[LightGBM] [Info] Number of data points in the train set: 17276, number of used features: 36
[LightGBM] [Info] Start training from score 20980.283283
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.85711 3425.94182 22991544.44568 4794.94989 0.85803 0.85711
1 Decision Tree Regressor 0.87497 2916.00390 20117380.73230 4485.24032 0.99975 0.87497
2 Random Forest Regressor 0.93534 2160.72752 10403286.69695 3225.41264 0.99064 0.93534
3 AdaBoost Regressor 0.67962 6242.86881 51549810.98551 7179.81970 0.68950 0.67962
4 Gradient Boosting Regressor 0.91387 2573.10671 13857960.41360 3722.62816 0.91646 0.91387
5 XGB Regressor 0.94052 2095.74801 9571242.05515 3093.74240 0.97335 0.94052
6 LGBM Regressor 0.93528 2212.02271 10413402.70582 3226.98043 0.95006 0.93528
7 Bayesian Ridge 0.85704 3425.95602 23002084.60878 4796.04885 0.85802 0.85704
8 CatBoost Regressor 0.94395 2048.07543 9018285.09373 3003.04597 0.96324 0.94395
9 SVM Regressor 0.01884 9164.00477 157871298.70750 12564.68458 0.01264 0.01884
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 6242.86881 3425.95602 2048.07543 2916.00390 2573.10671 2212.02271 3425.94182 2160.72752 9164.00477 2095.74801
MSE Score 51549810.98551 23002084.60878 9018285.09373 20117380.73230 13857960.41360 10413402.70582 22991544.44568 10403286.69695 157871298.70750 9571242.05515
R2 Score 0.67962 0.85704 0.94395 0.87497 0.91387 0.93528 0.85711 0.93534 0.01884 0.94052
RMSE Score 7179.81970 4796.04885 3003.04597 4485.24032 3722.62816 3226.98043 4794.94989 3225.41264 12564.68458 3093.74240
No description has been provided for this image 
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<  SezerDA8134's WORK  >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

[LightGBM] [Info] Total Bins 826
[LightGBM] [Info] Number of data points in the train set: 16155, number of used features: 50
[LightGBM] [Info] Start training from score 20101.304240
Regression Models R2 Score MAE Score MSE Score RMSE Score Train-Set R2 Score Test-Set R2 Score
0 Linear Regression 0.85638 3149.71584 20165033.00258 4490.54930 0.86728 0.85638
1 Decision Tree Regressor 0.86296 2728.23744 19241907.85753 4386.55991 0.99984 0.86296
2 Random Forest Regressor 0.92932 2031.29425 9923362.92751 3150.13697 0.99073 0.92932
3 AdaBoost Regressor 0.67402 5848.55769 45770506.88789 6765.39037 0.69588 0.67402
4 Gradient Boosting Regressor 0.90560 2491.45746 13255066.94846 3640.75088 0.91922 0.90560
5 XGB Regressor 0.94187 1921.59651 8161485.78555 2856.83142 0.97664 0.94187
6 LGBM Regressor 0.93480 2067.50864 9155117.74373 3025.74251 0.95344 0.93480
7 Bayesian Ridge 0.85621 3150.30680 20188581.34207 4493.17052 0.86726 0.85621
8 CatBoost Regressor 0.94710 1855.11993 7427973.24255 2725.43084 0.96701 0.94710
9 SVM Regressor 0.01552 8558.74385 138228278.83645 11757.05230 0.01452 0.01552
Regression Models AdaBoost Regressor Bayesian Ridge CatBoost Regressor Decision Tree Regressor Gradient Boosting Regressor LGBM Regressor Linear Regression Random Forest Regressor SVM Regressor XGB Regressor
MAE Score 5848.55769 3150.30680 1855.11993 2728.23744 2491.45746 2067.50864 3149.71584 2031.29425 8558.74385 1921.59651
MSE Score 45770506.88789 20188581.34207 7427973.24255 19241907.85753 13255066.94846 9155117.74373 20165033.00258 9923362.92751 138228278.83645 8161485.78555
R2 Score 0.67402 0.85621 0.94710 0.86296 0.90560 0.93480 0.85638 0.92932 0.01552 0.94187
RMSE Score 6765.39037 4493.17052 2725.43084 4386.55991 3640.75088 3025.74251 4490.54930 3150.13697 11757.05230 2856.83142
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